Coreference Resolution in Vietnamese Electronic Medical Records

Abstract

Electronic medical records (EMR) have emerged as an important source of data for research in medicine and
information technology, as they contain much of valuable human medical knowledge in healthcare and patient
treatment. This paper tackles the problem of coreference resolution in Vietnamese EMRs. Unlike in English ones,
in Vietnamese clinical texts, verbs are often used to describe disease symptoms. So we first define rules to annotate
verbs as mentions and consider coreference between verbs and other noun or adjective mentions possible. Then
we propose a support vector machine classifier on bag-of-words vector representation of mentions that takes into
account the special characteristics of Vietnamese language to resolve their coreference. The achieved F1 score
on our dataset of real Vietnamese EMRs provided by a hospital in Ho Chi Minh city is 91.4%. To the best of our
knowledge, this is the first research work in coreference resolution on Vietnamese clinical texts.

Keywords: Clinical text, support vector machine, bag-of-words vector, lexical similarity, unrestricted coreference